There are many applications where it is of critical importance to determine the amount of degradation that is or has occurred on a device. For example, in modern manufacturing techniques it can be critical for efficient manufacturing to know whether physical degradation, such as corrosion, is occurring on critical surfaces. As another example, in aircraft engines, the surface integrity of turbine blades is of extreme importance to safe and reliable operation. In all of these applications there is a strong need for effective techniques to determine if surface anomalies have formed, or are being formed on a device.
One particularly serious type of surface anomaly is pitting corrosion. Generally, pitting is a localized form of corrosion that is characterized by the formation of holes or pits on the surface. Pits often have a small surface area, and can thus be difficult to accurately detect or characterize. However, pits can penetrate to a relatively large depth and can thus cause severe failures in some applications.
Additionally, in some applications there is also a strong need to determine the size and rate of the growth of surface anomalies. For example, determining the rate of pitting corrosion growth can be used to calculate the structural integrity and hence the remaining component lifetime, a process generally referred to as component lifing. Without an accurate determination of the rate of anomaly growth may not be possible to determine the remaining component life.
Unfortunately, previous techniques for detecting surface anomalies in general, and pitting corrosion in particular, have had limited usefulness because they typically require an expert evaluation to interpret the data. This prevents, for example, the system from being used in an automatic feedback control loop. Thus, what is needed is a robust automated system and method for detecting and characterizing pitting corrosion and other surface anomalies.